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A study on brain tumor segmentation using convolution neural network

机译：卷积神经网络脑肿瘤分割研究

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Computer vision is playing important role in the field of human health care. This role is growing day by day. The application of computer vision techniques in health care has one of the aim to reduce human judgement in diagnosis. Thus, human error in judgement may be reduced. Brain related diagnosis demands at most care and a minute error in judgment may be disastrous. This makes medical imaging very important field. Various imaging methods like CT Scans, X-Ray, and MRI are available but MRI is the most reliable and safe. Even the smallest aberrances in the human body can be identified using imaging techniques. More preferred contrast information about brain tissues is provided by Magnetic Resonance imaging (MRI). Image segmentation is an important problem in medical imaging, which involves separating the tumor and organisms out of the medical data. Machine learning (ML) has gained enormous application with innovation in hardware requirements for computing. Convolutional neural networks (CNN) is one of the most effective techniques in ML. CNN has find applications in almost every field of research. CNN also find effective applications in brain MRI segmentation. In this paper, we present a study on CNN based MRI segmentation.
机译：计算机愿景在人类保健领域发挥着重要作用。这一角色日益增长。计算机视觉技术在医疗保健中的应用有一个旨在减少人类判断的诊断。因此，可以减少判断中的人为错误。大脑相关的诊断需求至关最多，判决中的一分钟错误可能是灾难性的。这使得医学成像非常重要。可以使用各种成像方法，如CT扫描，X射线和MRI，但MRI是最可靠和最安全的。即使是使用成像技术可以识别人体中最小的像差。通过磁共振成像（MRI）提供关于脑组织的更优选的对比度信息。图像分割是医学成像的重要问题，涉及将肿瘤和生物分离出医学数据。机器学习（ML）在计算的硬件要求中获得了巨大的应用。卷积神经网络（CNN）是ML中最有效的技术之一。 CNN在几乎每个研究领域都找到了应用。 CNN还在脑MRI分割中找到有效的应用。本文介绍了基于CNN的MRI分段研究。

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